Whole-genome shotgun sequencing strategy and assembly

全基因组鸟枪测序策略和组装

• 批准号: 8183944
• 负责人: David B Jaffe
• 金额: $ 85.95万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-26 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8183944
• 关键词: Achievement; Address; Algorithms; Base Sequence; Biology; Biomedical Research; Blood capillaries; Collaborations; Communities; Complex; Computing Methodologies; DNA Resequencing; DNA Sequence; Data; Defect; Diagnosis; Disease; Drops; Evolution; Foundations; Generations; Genes; Genetic; Genome; Goals; Grant; Health; Hereditary Disease; Human; Human Genetics; Human Genome; Individual; Industry; Institutes; Investigation; Island; Knowledge; Laboratories; Letters; Methods; Metric; Organism; Patients; Production; Reading; Repetitive Sequence; Reporting; Research; Research Personnel; Sampling; Technology; Time; Uncertainty; Variant; Whole-Genome Shotgun Sequencing; Work; base; cancer genetics; cancer genomics; capillary; comparative genomics; cost; falls; genome sequencing; human disease; improved; mammalian genome; meetings; new technology; next generation; pathogen; programs; standard of care; tool

DESCRIPTION (provided by applicant): The ability to read the DNA sequence of an organism's genome has revolutionized biology and biomedical research. Accurate assemblies of sequence data are critical because they provide the foundation for all subsequent work. Using capillary-based sequencing technology, high quality drafts were generated for many genomes. Over the past several years, massively parallel sequencing technologies have lowered sequencing cost by 1000-fold, but the reads from these technologies are shorter and less accurate than the capillary reads, hence harder to assemble, particularly for large genomes. We have recently demonstrated assemblies of massively parallel data that begin to approach the quality of those from capillary data. These assemblies were of genomes for which exceptionally high-quality ('finished') assemblies were already available, and we were thus able not only to rigorously assess the quality of our assemblies, but also to systematically diagnose their defects. Moreover we observe that in almost all cases, defective loci have enough coverage that they could in principle be assembled correctly, provided that the right algorithms were available. On this basis we have proposed a research program to develop computational methods for the creation of assemblies of unprecedented quality: In our first aim we propose to develop methods to achieve high quality draft assemblies of new genomes. Here our objective is to reach and exceed the level of quality that had been achieved using capillary sequencing. In our second aim we will develop methods to achieve ultra high quality assemblies of human genomes. To do this we will leverage the existing human reference sequence and reference sequences of other individuals, including those that we would create. In this way we aim to achieve near-finished quality for regions represented in the reference sequences (essentially via 'resequencing' methods), and at the same time (by de novo methods) capture those regions that are not present in the reference sequences. Our aim is thus to produce the best possible representation of each individual's genome. We note that as costs drop, this is likely to become 'standard of care' for patients. In our third aim, we look beyond existing data, to the next generation of sequencing technologies, to assemble very hard regions using very long and 'strobe' reads. These hard regions include segmental duplications, which are evolutionary hotspots, associated with many diseases, and inaccessible to current methods, except those using very expensive clone-by-clone sequencing. Finally our fourth aim is to make assembly methods accessible to the community. Here our goal is to make it as easy as possible for a range of users (including individual investigators) to match the results achievable by genome assembly experts. In short, through our four aims, we will enable the community to achieve the highest possible assembly quality using the lowest cost data. We thus anticipate that our work will advance a broad range of investigations of importance to biology and human disease. PUBLIC HEALTH RELEVANCE: This grant will develop better methods for completely and accurately determining the genome sequence of an organism, in particular producing precise representations of complex and repetitive regions of genomes. This will advance a broad range of investigations in genome evolution, cancer and human genetic disease.

描述（由申请人提供）：读取生物体基因组DNA序列的能力已经彻底改变了生物学和生物医学研究。序列数据的准确组装是至关重要的，因为它们为所有后续工作提供了基础。使用基于毛细管的测序技术，为许多基因组生成了高质量的草图。在过去的几年里，大规模并行测序技术已经将测序成本降低了1000倍，但是这些技术的读数比毛细管读数更短且更不准确，因此更难组装，特别是对于大基因组。我们最近展示了大规模并行数据的组装，这些数据开始接近毛细管数据的质量。这些组装体的基因组已经有了非常高质量的（“成品”）组装体，因此我们不仅能够严格评估组装体的质量，而且还能够系统地诊断它们的缺陷。此外，我们观察到，在几乎所有情况下，缺陷基因座都有足够的覆盖范围，只要有正确的算法，原则上它们就可以正确组装。在此基础上，我们提出了一个研究计划，以发展计算方法，创造前所未有的质量组装：在我们的第一个目标，我们建议发展的方法，以实现高质量的草案组装的新基因组。在这里，我们的目标是达到并超过使用毛细管测序所达到的质量水平。在我们的第二个目标中，我们将开发实现人类基因组超高质量组装的方法。为了做到这一点，我们将利用现有的人类参考序列和其他个体的参考序列，包括我们将创建的那些序列。以这种方式，我们的目标是实现参考序列中代表的区域的接近成品的质量（基本上通过“重测序”方法），并且同时（通过从头方法）捕获参考序列中不存在的那些区域。因此，我们的目标是产生每个人的基因组的最佳代表性。我们注意到，随着成本的下降，这很可能成为患者的“标准护理”。在我们的第三个目标中，我们超越现有数据，着眼于下一代测序技术，使用非常长的“选通”读取来组装非常困难的区域。这些硬区域包括片段重复，这是进化热点，与许多疾病相关，并且除了使用非常昂贵的克隆测序的那些方法之外，目前的方法无法获得。最后，我们的第四个目标是让社区可以使用组装方法。在这里，我们的目标是使一系列用户（包括个人研究者）尽可能容易地匹配基因组组装专家所获得的结果。简而言之，通过我们的四个目标，我们将使社区能够使用最低的成本数据实现最高的装配质量。因此，我们预计，我们的工作将推动生物学和人类疾病的重要性广泛的调查。 公共卫生关系：这笔赠款将开发更好的方法，以完全和准确地确定生物体的基因组序列，特别是产生基因组的复杂和重复区域的精确表示。这将推动基因组进化、癌症和人类遗传疾病的广泛研究。